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Enregistrement W2626712504

Vividness and Behavioral Specificity in Visual Imagery: Not what you’d expect

2009· article· en· W2626712504 sur OpenAlexaboutno aff
Amedeo D’Angiulli

Notice bibliographique

RevueeScholarship (California Digital Library) · 2009
Typearticle
Langueen
DomaineNeuroscience
ThématiqueFace Recognition and Perception
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychologyMental imageCognitive psychologySet (abstract data type)Object (grammar)Everyday lifeFace (sociological concept)Cognitive scienceCognitionEpistemologyArtificial intelligenceComputer scienceSociology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Vividness and Behavioral Specificity in Visual Imagery: Not what you’d expect Amedeo D’Angiulli (amedeo@connect.carleton.ca) Department of Psychology, 1125 Colonel By Drive Ottawa, ON, K1S 5B6, Canada and “dorsal” imagery, and the involvement of the complex underlying working and long-term memory dynamics. A major threat to the vivid-is-fast relationship is that it may really reflect various types of participants‟ expectations during lab experiments, not at all generation and use of mental images. Following up to previous research (D‟Angiulli & Reeves, 2005), I show that the current evidence on the vivid-is-fast relationship, and its selective variations in some conditions, is incompatible with the main accounts based on expectations and tied to the alleged epiphenomenalism of imagery experience. In addition, presenting evidence from multiple measures, I show that vividness fits well within the causal theory approach to validity (Borsboom, Mellenbergh & van Heerden, 2004). To explain the data reviewed here (as well as other recent literature evidence), I develop a minimalist approach, dubbed vividness-core principle. This consists in a parsimonious set of propositions that: 1) builds on the vivid- is-fast relationship and Levesque‟s (1986) formalization of vividness in AI; 2) accounts for most everyday imagery, explaining how imagery could be useful for everyday incidental memory and undetermined object-based reasoning. When remembering specific everyday objects or events linked to past experiences, for example personal events (e.g., the face of a relative or a pet), people generally report “seeing with the mind's eye”. A pervasive aspect of people‟s report is the vividness of their mental images. Images may come from the imagination (e.g., a pink dog) or from retrieved episodic and specific representations which refer to everyday objects (e.g., your breakfast this morning). Setting aside “imagination imagery”, whose vividness is entirely subjective, we can define the vividness of “realistic” imagery as: (i) The extent to which mental images reflect the composite quality (including specificity, detail, and richness) of visual representations that would have been generated if the object had actually been perceived. Proposition (i) requires no presumptions about the underlying format of mental images (e.g., propositional) other than that they are a type of analogue. All that one needs to assume is some elementary properties of databases (Brachman & Levesque, 2004). That is, a memory database containing information about a given domain (of objects and relationships between these objects in the world) will contain individual images that consistently designate individual objects in the world and relationships between individual objects that designate the respective relationships in the world. Second, according to (i), vividness can be interpreted as a crude proxy for what is available in the memory database, a report about a represented object X or relationship involving X will be more or less vivid depending on the extent to which information about X is perceived to be complete (see Levesque, 1986), in turn this should be reflected in behaviour, for example, the time needed to respond to a query about X. Some recent research (D‟Angiulli, in press; 2002; D‟Angiulli & Reeves, 2007; 2002; Reeves & D‟Angiulli, 2003) has shown conditions in which the relationship between vividness ratings and image latency response reflects some properties of the visual systems: the system that is dedicated to process object-properties (ventral pathway) and the system that is dedicated to process locative properties of mental images (dorsal pathway). In particular, the results of these studies showed that for small images expected to recruit mainly the ventral pathway (i.e., requiring size-scaling of less than 10 o ) the higher the rated vividness, the faster their generation. This vivid-is-fast relation, it was also found, changed for large images expected to recruit mainly the dorsal pathway (i.e., requiring size-scaling of 10 o or more). While the size-dependent effects gradually disappeared over the course of repeated image generation, the vivid-is-fast relation remained, although it corresponded to a much weaker effect. Based on these findings, it was concluded that differential patterns of vividness-image latency relationship can reflect “ventral” References Brachman, R.J. & Levesque, H., J. (2004). Knowledge, representation and reasoning. NY: Morgan & Kaufmann. Borsboom, D. Mellenbergh, G. J., & van Heerden, J. (2004). The concept of validity. Psychological Review, 111, D‟Angiulli, A. (in press). Is the spotlight an obsolete metaphor of „seeing with the mind‟s eye”? A constructive naturalistic approach to the inspection of visual mental images. Imagination, Cognition & Personality. D'Angiulli, A., & Reeves, A. (2007). The relationship between self-reported vividness and latency during mental size scaling of everyday items: Phenomenological evidence of different types of imagery. American Journal of Psychology, 120(4), 521-551. D‟Angiulli A., & Reeves A. (2005). Picture theory, tacit knowledge or vividness-core? Three hypotheses on the mind‟s eye and its elusive size. Proceedings of the 27 th Cognitive Science Society Annual Meeting, 536-541 D'Angiulli, A. (2002). Mental image generation and the contrast sensitivity function. Cognition, 85, B11-B19. D‟Angiulli, A., & Reeves, A. (2002). Generating mental images: Latency and vividness are inversely related. Memory & Cognition, 30, 1179-1188. Levesque, H. (1986). Making believers out of computers. Artificial Intelligence, 30, 81-108. Reeves, A., & D‟Angiulli, A. (2003). What does the visual buffer tell the mind‟s eye? Abstracts of The Psychonomic Society, 8, 82.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,008
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,002
Communication savante0,0010,002
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,046
Tête enseignante GPT0,287
Écart entre enseignants0,241 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2009
Routes d'admission1
Résumé présentoui

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